Unraveling the impact of financial stress and trade policy uncertainty on advancing renewable energy transition in the USA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Renewable energy consumption (REC) has become the most suitable option to tackle the issues of energy security and climate change because it is a sustainable, clean, and affordable energy source. Literature on the determinants of REC is growing rapidly, but most rely on linear analysis. This analysis is a nonlinear perspective on the impact of financial stress and trade policy uncertainty on REC in the USA over 1995Q1-2021Q4. The study uses autoregressive distributed lag and nonlinear autoregressive distributed lag for empirical analysis. The linear estimates reveal that financial stress and trade policy uncertainty reduce long-run (LR) REC. On the other hand, the nonlinear estimates suggest that positive changes in financial stress and trade policy uncertainty reduce REC, whereas the negative changes in both these factors boost REC in the LR. While the GDP causes an improvement in REC, environmental technologies do not significantly impact the REC in the LR. In the short-run, only the linear and nonlinear estimates of financial stress and environmental technologies significantly impact REC. Due to the asymmetric nature of the findings, policymakers must take into account the positive and negative changes in the financial stress and trade policy uncertainty while devising policies to promote renewable energy transition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it